This repository contains code for the Optimal Loadings feature selection technique proposed in the following paper pdf.

@inproceedings{NagarajaPLS15,
  author    = {Varun K. Nagaraja and
               Wael Abd{-}Almageed},
  title     = {Feature Selection using Partial Least Squares Regression and Optimal
               Experiment Design},
  booktitle = {International Joint Conference on Neural Networks, {IJCNN}},
  year      = {2015}
}

The determinant maximization is performed using a modified version of the candidate exchange function present in the MATLAB Statistics Toolbox. Since I cannot share the original source of the MATLAB function, I have created a proteced file. Contact me if you want to know the edits.

Minimum Redundancy Maximum Relevance (mRMR) (mRMR.m)

• Needs external library. See mRMR.m for details.
• Download a newer version of the mutual information toolbox

Partial Least Squares (PLS) regression coefficients (regCoef.m)

• Uses plsregress.m from MATLAB statistics toolbox

ReliefF (classification) and RReliefF (regression) (relieffWrapper.m)

• Wraps around relieff.m from the MATLAB stats toolbox. This is available MATLAB r2010b onwards.
• Another option for ReliefF is to use the code from ASU Feature Selection toolbox. This uses ReliefF from weka toolbox and hence needs additional libraries. Please see the corresponding documentation.

Fisher Score (fisherScore.m)

• Wraps around fsFisher.m from the ASU Feature Selection toolbox

• Load the data
• Create an options structure using featSelOptions.m
• Perform experiments using compareFeatSelAlgos.m